Application of Artificial Neural Network for Predicting Microbiological Pollution in Fresh Water

The classical methods for detecting the micro biological pollution in fresh water are based on the detection of the Escherichia coli bacteria which indicators of contamination. Some of them are based on simple and easy-to-handle concepts like laboratory methods. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study an attempt was made to develop a new approach for the detection of Escherichia coli bacteria in fresh water is proposed. This is done by the Artificial Neural Networks (ANNs) in which the performance was measured in two ways, training and testing. The artificial neural networks prediction is proposed based on effect of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity-of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors. The superiority of the ANNs method is due to high prediction accuracy and the ability to compute combined effects of physical and chemical factors on the fresh water induced during the bacteria growth. This tool can be used in the first time for predicting the micro biological pollution in water.